Ep.114 Mastering AI Sales Strategies for Tech Advisors in a Rapidly Evolving Landscape with Jason Lowe

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Join us for this special episode about Mastering AI Sales, where we dive deep into AI and what’s coming in the near future as we understand the incubating AI companies that are in the mix already in Silicon Valley. We explore a great AI Tsunami tweet out there by Chrys Bader. It’s 157+, to be exact! Listen in as Jason Lowe and I break down these product areas, understand existing products in the channel, how the customers can improve current technology, and, more importantly, how we talk to customers about them and meet them wherever they are on their AI journey.

Welcome to the podcast designed to fuel your success in selling technology solutions. I’m your host, Josh Lupresto SVP of Sales Engineering at Telarus and this is Next Level BizTech. Hey everybody, welcome back. We’re here talking about AI this week, everybody’s favorite hot topic, specifically mastering AI sales with some strategies for the tech advisors in this rapidly evolving landscape. Today on with us, we’ve got the wonderful, the amazing Mr. Jason Lowe also goes by J. Lowe. J. Lowe, welcome on, man. Thank you, it’s great to be here. So, solution architect here at Telarus man of many talents, CX, AI, all kinds of things, you know, and if people missed to get to know you a little bit, your backstory, go back to Episode 61, they can get caught up on some of that, but give us a little highlight here on kind of how you got started into this field and then really how you guys are passionate around AI. So, I joined theCCaaS space around 2008 after doing a lot of different roles in the past, including writing code and a few other technical type positions and spent a decade at a small company that eventually became nice to engineering, did things in product management, went to another CCaaS provider named Talkdus for a while, and it was shortly after that when I was languishing at a competitor to Telarus when you and I started speaking and now I’m on your team and I feel great about it, it’s great to be here. Love it, love it. The history at nice CX1 and yeah, it’s been a, it’s been a wild ride kind of in this landscape and now here we are talking about AI, CX and everything beyond. So, I wanted to put this episode together because I feel like we should just record some of our own conversations because we get so excited about some of these. But I read this article, I’m a big Twitter fan, there’s an article out there, this guy’s name’s Chris Bader and the article was titled “The AI tsunami is coming” and it relates back to the batch of W24 contestants that came out of Y Combinator and what that is for anybody that doesn’t know, right, is Y Combinator is this Silicon Valley started in early 2005 and really everybody thought, “Jesus, if we could put all the things that people need to run great tech companies and learn and grow and entrepreneurs to be successful, we could probably incubate some great tech companies.” So, there’s incentives along the way, there’s valuations at the end of it, all these great things and so this thing has evolved. Find a tech company, you know, any tech company you could think of from Stripe to Airbnb to OpenAI and Sam Altman was one of the first ones that kind of went through this but so this is a real true blue organization. I think at this point there’s five, six hundred billion evaluations of companies that have gone through this. So, this is no tiny thing, right? This article goes on to talk about in this W24 batch, this latest winter batch, there is a hundred and fifty seven AI startups in here at JLo. So, if you think back what we looked at, you know, let’s flashback UCAS, CCAS 10 years ago, right? Maybe we had a hundred and fifty seven UC providers, CC providers, right? But you have this natural effect, call it just Darwinism, call it the evolution of the market, call it whatever it is and we don’t have as many as we used to, right? Just because of natural changes and so anyway, you and I were talking about this and there’s a couple themes in here and so I want to talk about these themes a little bit and kind of get your feedback on them as we go because that’s going to drive how I think we help with some of these strategies because we know the same history in the past programmatically is gonna follow suit and we know this is what’s coming to market, right? If 600 billion is in there, that’s gonna come to market and AI and productization is the next theme. So, let’s talk about it. Six themes in here. One, AI is revolutionizing software dev, right? What our buddy Elon, Papa Elon said AGI maybe as soon as next year. What do you think about that? Yeh-nay? You might be right. I think he’s right. I mean, let’s just say that the momentum is getting kind of crazy to see how this, you know, approximation of more law, the slope is just going more and more at a 90-degree angle. It’s just getting a little bit crazy. I mean, as far as what it’s doing in software dev, I mean you have Devin now who is an autonomous agent that can actually do all sorts of things as it relates to code generation and assistive processes and research and testing and debugging and things like that and that’s not the only autonomous agent out there that’s doing things like that. So, software development, I mean, companies now that are hiring developers, about two-thirds of them want developers that actually utilize AI to help them write code because it can save so much time, make them more efficient, can help in debugging and make their code a little bit better. It’s just a really fun and exciting time if you’re a dev in AI. Let’s talk about this too, right? There’s these phases of autonomy and these phases and evolution of AI and this one that’s out here kind of at the end when utopia and rainbows and all these great unicorns pop out which is AGI. Can you break down a little bit about this magical thing that we’re all talking about now with open AI and chat GPT and what that has birthed but then what we’re working towards, what all these entities are working towards, what is now, what is AGI? Yeah, you bet. So, artificial intelligence is commonly classified into a couple of different categories with a third category that we haven’t reached yet or even come close to and the second category is what we’re approaching and so that first category is ANI or artificial narrow intelligence where we have artificial intelligence algorithms and applications that work towards a specific goal in a very focused area. In other words, it’s narrow in focus but what we’re finding is that we’re approaching this artificial general intelligence level which means that we’re going to have some sort of a computer entity or something that can basically do everything that a human being can do on a computer. Whether that means having emotions, whether that means, you know, all of these other things related to being a self-aware entity, we’re not sure but everybody that has been looking at AI recently has remarked about how many of these generative AI, large language models, and other things are passing what’s called the Turing test which is something that makes it so that you determine if it actually is self-aware and if it has a higher functionality in the way that it processes things kind of like a human being does and it is now more or less becoming commonly adopted and agreed upon that we’ve reached that point where there are AI entities out there that pass the Turing test. That’s so cool. Alright, let’s talk about the next one. Customer service. I mean, you know, innately, right, this hits your bucket coming into the CX spaces. You’ve seen CX productization grow, you’ve seen other providers come into the space, you’ve seen existing suppliers kind of bolt on other technologies. But what do you see in here in point number two, AI with regard to customer service? I think it’s going to be a very interesting 24 to 36 months because Gen AI agents are becoming a thing. And so are autonomous agents as far as being able to go and execute different tasks and things like that. If you ask me personally, I mean, this is just Jason saying this, Josh, it’s not I’m not a scientist, I’m not someone that’s going to try and read the future, but it’s going to have a major effect on customer service in that the lines will be blurred. Things are getting so good now that people can’t tell that they’re talking to a machine. The voices are so good. The lack of latency is there. The ability for the machine to riff or to do things off the cuff is drastically increased. And so in text based communication, it’s really hard to tell if you’re talking to a human being or a machine right now, voice is getting there. And the thing that we need to remember is that if we have these autonomous agents that can execute and do everything on a computer that an agent can do, well, what makes it that you’re not having the AI do all of the things because AIs don’t get sick, they don’t take vacation, they don’t have bad days, they’re not cranky in the morning, you know, all of these different things, AIs are going to be very consistent. And so who knows? I mean, it’s quite possible that seat counts for CCAS platforms may go through some sort of a net reduction rather than a continued increase like we’ve seen happening. It’s going to transform customer service in a lot, a lot of different ways, mostly in the way that companies interact with their customers through this technology, not just having it as an assistive technology.

Fair. So let’s talk about I think the great thing about that is that, you know, even if there’s bolted in inherent, you know, AI doing sentiment analysis, because we’ve got these intelligent agents, I think the beautiful thing in that still is that the technology is so complex, that the harder this gets, the more the customers need the partners, the more the we all need each other, right to help in these conversations, because now it’s going to become not what’s the difference between platform A and platform B, it’s the difference of does this one give me AGI? Does this one give me you know, Gen AI? Does this bolt on sentiment? I mean, it’s the complexity is wild, right? So I love complexity is great for this channel. And we should get excited when we see some of those things for sure.

I agree completely.

Let’s talk about I’m not going to go into all all six, seven of these, but let’s just talk about one or two more real quick. I think there’s cool stuff. Obviously, this is a big win, right? You know, we all want to see diseases get cured, we all want to see computational power and AI and all these, you know, all these GPUs, tackle things, drug, drug discovery. We were more nerds, and we text about these things. We were texting about a show that we watched that AI did, you know, image analysis and radiology detection, right? I mean, sky is kind of the limit on that. Sync all the money you can into that so that some of these things can be detected on scans earlier before the human eye even catches them, right?

Yeah, absolutely. I mean, the radiology stuff applies to all sorts of radiology, not just x rays, not just MRIs or anything like that. I mean, it’s gotten to the point now where it can discover what kidneys are prone towards kidney failure or different types of things like that. We all have heard the stories of identifying lung cancer well before lung cancer actually was diagnosed by a human doctor. But it’s not just limited to radiology. AI is also being assistive in diagnosing pretty heavily as well. In fact, it’s one of the more fun use cases that I like to talk about is they have discovered that AI can detect with an 80% accuracy as to whether or not an individual is type 2 diabetic, just from listening to their voice. And that’s it. And given that 50% of the cases of type 2 diabetes in the United States today go undiagnosed, that’s a pretty big deal. Pretty big deal. Yeah, good stuff there. And, you know, last couple thoughts on this. Obviously, you know, the next biggest use case, you know, think of due diligence and finance investing, CAD modeling, you know, build me a 24 floor, you know, residential complex, whatever, you know, that kind of thing. And then I think the one that came out really quick, right, it’s your new creative partner, videos, content, scripts,

make me a video where we’re, you know, going through the rainforest and blah, blah, you know, all of these things that took so much cost, and so much right, that, like, this is not, this is not us making this is not Jason and Josh making up six cool things to talk about. This is where the money is about to be spent in these key areas, right. So awesome stuff to see where this productization is going. And that’s what we’ll get to it kind of the next steps of this is, all right, how do we talk about this? How do we how do we get forward with the customers and progress these conversations along because it is a different talk track. You know, it’s a much different talk track. And look, they’re all struggling with it too. Everybody’s trying to figure this out. Right. Not a lot of these guys have, you know, AI and LLM engineers and things like that. Right. So let’s, let’s talk about this, then. Million dollar question, as we go forward, is AI a product? Or is it an ingredient of a lot of different type of products?

I’m gonna say ingredient. And here’s why. It’s it’s a very crucial ingredient. In fact, I would say it’s, you know, in using the word ingredient kind of doesn’t do it justice. Let’s talk about boats. Okay, let’s talk about naval boats for a while. I mean, the very first naval boats that were out there, even during the Civil War and things, they had steam engines in them. And then there was a big revolution when diesel engines and fuel driven engines suddenly upped the capabilities of these warships to do all sorts of crazy things. And then nuclear powered engines come along and we have nuclear powered subs, we have nuclear powered, you know, aircraft carriers that make it so that they can stay at sea for a lot longer, they can do what they need to do on this virtually infinite power that cut well, not infinite, but that comes from, you know, radiation technology. I think that AI is the leap from the steam engine to nuclear powered engines. It’s not insignificant. And so it’s, it’s kind of not doing it justice to say ingredient, because it’s such a core important element that’s going to bring so much power, but nevertheless, a boat to boat, right? What you’re doing is you’re using this incredible technology to make the boat so much better than it was. And so amazing. But it’s still just a boat. That’s the role that AI plays today. Yeah, I think that’s fair. And look, I think being in the middle of these paradigm shifts to is always hard to see how impactful it is. So certainly, in hindsight, we’re all going to be geniuses. But if you do flash forward, and you think about the rate of change and the evolution, and then you look back, I mean, it’s it has to be similar to what Ford went through in going from the horse carriage to the car, you had plenty of people that are like, yeah, but my, but my horses are fine. Like, why would I need that? Right? I can’t envision that. And then you read these articles that talk about, you know, funny, there’s a byproduct when you have a, you know, a million horses on a dirt road turns out, it gets a little smelly after a while. And so some people are like, well, okay, you know, maybe, okay, you know, there are these ways that people’s eyes get opened up when you start to see some of these use cases. I think it’s it’s interesting. And I think this is, this is that next one. So I love it. And that’s, I think that’s really what that’s what you do. That’s what we do. The team does is we try to distill these down as these 157 AI startups and the next 500,000 come into the space. Are they products? Are they productized? Can we sell it? Is it a bundle? I think that’s the fun part of this gig is trying to help get that and get those things out to market, which is what we’re spending so much time on. Let’s let’s talk about generative AI. First of all, help us understand what generative AI is. And then and then we’ll get into, you know, can we sell it? And really, where do these offerings start to exhibit?

Okay, so generative AI is a specific type of AI that you would think is generating new and unique things based on the output that you get. In actuality, what it’s doing is it’s sampling from what it’s previously been trained on, which a lot of people don’t understand. But are human beings very different? No, I mean, human beings do have true creative flair and can do things completely out of the ordinary and out of the blue. But really, by and large, a lot of us are imitating what it is that we have learned before. And generative AI is just the same thing. We’re feeding it tons and tons of information. And it’s going back and it’s mathematically trying to predict what it should tell you next. In the case of text generating, you know, generative AI, otherwise known as a large language model or an LLM, that’s all it is is a mathematical algorithm taking what it is that you fed it, comparing that to the training set that it’s had, and then trying to mathematically predict what it should say next. It’s not thinking about it, per se. It’s not really having any sort of a consideration step before it spits it out. It’s just using a mathematical algorithm. Now, there are other things out there that are making generative AI a little bit more accurate. And these different steps that are being put into place like retrieval augmented generation, that goes and checks a data source and then might incorporate the data that it finds to make sure that its answer is more accurate and true. And that has been proven to really increase accuracy. But nevertheless, it’s still it’s just it’s just math. That’s what’s happening right there. So talk to me about, you know, I think we saw this early on manifest itself in CX, right? Cool things started to get bolted on to CX products, new CX suppliers started to come into the space, what we saw that in sentiment analysis, tonal analysis, you know, next agent, recommendation, things like that. Where else do you see that? I mean, obviously, it’s huge in the CX, but maybe help us understand for the partners listening out there. Where else do you see that exhibit in current offerings of suppliers?

Well, I mean, it’s prominently in the CX space. I think that’s probably as you mentioned, kind of the place where it’s the most product isable, it’s the easiest to sell, it’s the easiest to recognize. But you know, generative AI, one of the things that’s great about generative AI is that it’s able to recognize patterns. And people have figured out that it can recognize patterns. And so using these MLDL or machine learning, deep learning instances that are built around generative AI algorithms, you can use those for a variety of other features instead of just, you know, graphics generation or text generation. And so it’s driving innovations and things that require pattern recognition, like physical security. I mean, it would be really neat to have AI engines that could recognize someone, just based on the way that they move, not necessarily having to do facial recognition, but maybe the way that they walk, the gestures that they make, and so forth. And that’s out there. And in physical security, we have firms that are in the teller’s line card that sell these systems that can do person recognition, not facial recognition, person recognition. And how do you apply that? Well, let’s look for people that are in the back storage room. And if they’re not a known employee, why are they there? Are they there to steal something? Are they there to cause damage? Are they there for some other sort of nefarious activity? That’s a big deal. You know, pattern recognition, at least as far as traffic is concerned and retail is a really big thing. And so yeah, that whole physical physical security or physical observation of people and patterns in presence and patterns in behavior is really a big thing. And then in cybersecurity, that is kind of morphing into an extension of that type of behavior. You know, one of the providers that we have in our line card now is basically all about biometric authentication of individuals without using a fingerprint or a password or a face print. We’re talking about, you know, biomechanical or kinetic, if you will, authentication of who individuals are based on things like the words they use when they type or how they type, you know, how fast they type certain keys, different things like their position relative to other objects that they’re integrated to via Bluetooth. I mean, it’s, it’s really interesting to see how this stuff is happening. And this pattern recognition is kind of morphing itself into doing a lot of things that we, as humans do so, so incredibly well, right? This is what makes us different from a lot of different animals is that we can recognize patterns and we can act on the things that we recognize much more rapidly and much more effectively.

Talk to me about the cloud side of that. I mean, we’re seeing this get bolted on. But what about for those customers that go, you know what, I want to build this, I want to build our own open AI, I want to build our own LLM, right? How productized is that already?

It’s productized in the sense that there are providers out there that will do it, and that they will certainly help and they will consult through these use cases. We’re in a really interesting timeframe right now, because there’s this big spectrum of AI projects or these AI possibilities. And on one, and you have the highly productized things like CX, like, you know, physical security and computer vision applications. And then on the far side, you’ve got this 100% customized, only going to see it once machine learning, deep learning use case that needs to be built from the ground up. And where we’re getting things filled out now is that big realm in the middle, there’s a lot of stuff that’s coming. We’re seeing more use cases applied. A lot of these companies that are doing these use cases, if they do enough of them, they’re going to start recognizing enough to be able to generate things that are a little bit more packaged and other different verticals or other different practice areas or processes. And so that’s kind of coming. But right now, it really is all about, let’s identify the problem. Let’s identify what we want the end case to be. And then we’ll take the AI and figure out the steps in the middle that we can use AI to make that happen, which is really kind of different than the way things are now, right? Let’s identify the problem. Let’s identify the solution. And then let’s see if it meets our end case that we’re after.

Yeah, yeah, it is different. You know, I think the thing to think about to your point is that, do you want to build something? Does a customer say I’ve got AWS and I want to use bedrock as the LLM? Awesome, we can. You want to use co-pilot and they’re in Azure? Want to build that out custom? Absolutely, we can. Right. And those boxes continue. And, you know, that’s what we’re talking about a lot of these events and, you know, helping people understand productization, where the suppliers fit. But to your point, every one of these conversations that we’ve been in is, okay, but let’s step back. What’s the desired business outcome? What are we trying to get to? Is the data structured right now? Do you have a good data set? Right? Bad data in, bad answers out. And so to your point, it is, let’s walk that back. And let’s just have that, that early conversation. And it’s going to take a little bit to kind of get some of those things implemented. But the good thing is, we can meet the customers wherever they are, right? It doesn’t, doesn’t matter whether they’re completely not sure what to do, or they’ve already got an LLM figured out an OEM per se.

And we can help them with that. So I’m excited to kind of where that’s at already. It’s great to see our suppliers stepping up in that. Alright, let’s talk about, let’s play on this whole tsunami thing. Let’s shift to tidal waves. Let’s talk about big challenges that you see for the partners with all this flurry of information, this AI tidal wave. We’re really, really creative here with our words. So biggest challenges you see for the advisors, right with this AI tidal wave?

I think probably A number one, the biggest challenge is fear. I’m just calling it what it is, is AI is scary. AI is scary to a lot of people. And a lot of that could be born of the way that Hollywood has portrayed AI for years and years. And a lot of it could be born of the fact that a lot of experts are wondering, what is the effect of AI going to be? Is it going to make humans obsolete? Is it going to, you know, what’s going to happen there? But and so there’s just a bunch of fear surrounding it. And I think tech partners today need to realize that they don’t have to understand everything about AI in order to dive in and be able to work with AI solutions with their customers, they just have to know enough to be able to talk the talk a little bit and be able to identify what’s needed. And then go and figure out what providers are going to work with. So you know, the fear, the biggest thing is getting through that first one, you know, it’s interesting, because slinging circuits and the like, you know, getting into the CX space, a lot of people that I’ve talked to that have wanted to get in the CX space, say the reason they don’t is just because they just don’t know it. And so now they feel like that’s limiting them because they feel that AI is really exhibiting itself in the CX space. And so they’re asking, what’s the lowest hanging fruit? How can I get into AI as quick as possible? How can I start feeling comfortable with implementing AI? My first question to them is, are you selling CX? Are you doing something with these highly productized, easy to show ROI applications that can do these things? And if the answer is no, then you’re asking your I’m asking them, you know, take a look at that, see if that’s something that you can get to know a little bit better. That’s going to help you overcome that fear, because then once you’ve been through an AI implementation of some product, you’re not going to be as hesitant the next time to maybe perhaps look at a different use case, it’s something that you’re going to get into and, and customers are really diving into it. And so the biggest challenge will be keeping up with the customers. Quite frankly, there’s a lot of customers that are going to their tech advisors and saying, you know, look, dude, I’ve been designated the AI champion, and I’m supposed to implement AI everywhere I possibly can, where do I even start? And that’s an actual thing. I mean, Biden, our President Biden has now designated that every major organization in the federal government needs to have a chief artificial intelligence officer, because the permeation of artificial intelligence capability that’s going through just about every possible job in the government, requires somebody that knows what the heck he’s doing and can identify where AI can be applied that can create that AI roadmap that can figure out the strategy that companies need to use, or in the case of the federal government divisions of the government, to use AI to make things better. And so advisors, the biggest thing that you have to do is stay up on it so that they don’t pass you. It’s an instance where customers could eventually start using AI to figure out where to use AI. I mean, how often is, how often is that going to happen quite a bit, I think? Yeah, let’s let’s talk about that for a second. So, Tularis Commercial here, I would say, regardless of where they are, just like with any other technology that they’ve sold this, not sold that, sold this, not sold that, it doesn’t matter. We’ll meet you wherever you are, and help you on that journey. And I would say, part of that journey is Tularis University. There is a ton of content in there, with whatever swim lane from AI to CX, and, you know, all these other things that they can learn up on, right? There’s a lot of great learning paths in there. But I mean, you know, we’re, we’re talking about some of these things, because this is our regular talk track, whether we’re just talking internally, because we’re nerds, or we’re with partners, and we’re with customers. And these are all real examples. But talk for just a second about when somebody pulls a member of our team in, in a conversation like this, I don’t care if it’s POS lines, I don’t care if it’s AI.

Maybe just remind everybody of where and how we help with that.

Well, that’s the whole reason for my existence in Tularis as a solution architect, to be honest with you, I, I’m supposed to be here to be your subject matter expert, if you’re a tech advisor, working with Tularis. And this is something that I think is relatively unique is the strength of the engineering bench at Tularis. It’s really quite remarkable.

It’s, it’s nice to be counted amongst a group of such capable individuals and actually considered to be one of them. I’m not sure that I deserve it sometimes, but I’m quite glad to be here because we can be that SME, we can be someone who once you identify there’s the possibility of an implementation of some cool technology, whether that be AI or something else, go to your engineering group and let them help you, let them sit beside you in those meetings, let them represent you, let them be the person that you lean on. It’s, it’s, it’s really neat to do that because we get to see these partners as we’re sitting by them, learn and grow. I mean, if you want to get into an area that you don’t know very well, don’t go into it alone. Grab one of us and we will go through the doorway with you and make sure that you’re doing all right before we, you know, push you off without your training wheels and then you can kind of

carry yourself on forward. But that’s the whole reason the engineering department started to begin with. Yeah. And, and, and I would say too, we’re modular in that fashion. I get asked this question a lot. If you want us on Discovery calls with you to help go and talk to ABC customer and that’s one call, that’s five calls, that’s whatever. Great. If you want to pregame and just talk about maybe it doesn’t make sense for us to be on that call, you’re just going to go into a lunch meeting and you want to, you want to help draw some of these things out. And we’re going to talk about that here in the next area. Great. Uh, you know, and, and, you know, we don’t have to have that to Laris badge on we wear a lot of different badges. And I think that’s just, it’s important to know that our partners know we’re just an extension of them at the end of the day. We’re just here to help them be successful in all these different technology areas. So good, good stuff there. Um, all right, let’s talk about, let’s talk about I’m a partner. I’ve got an existing base. You’ve gotten me excited that there’s a lot of stuff out there with regard to AI, AGI, ABC, whatever it is. Um, all these different things. How do I walk away from this? Uh, after I go and give this podcast a lovely review and a great star rating and tell all my friends about it. How do I then go talk to my existing customer base about this?

That’s a good question. And the bottom line is you just need to go in and throw around the words artificial intelligence. I’m sorry, but right now it’s about that simple because as we mentioned before, companies are trying to figure out what the heck to do. And it’s, it’s really interesting because there, there are some actual facts and figures surrounding this. Um, there’s a certain percentage of companies that are getting into AI specifically to address specific use cases. And there’s also a certain percentage of companies. That are getting in just to keep up with their competition because they have heard that their competition is starting to utilize AI and specific use cases. 35% are the ones that are looking to keep up with trends. 31% are looking to come up and keep up with competitors. That’s a big thing. So whether or not they have the use cases or not, whether they have identified how AI can help their business virtually all companies are taking a hard look at how they can do it and they need help. They need guidance. They’re going to their tech advisors and seeing if it’s something that they can help with because there was a big survey done by GBK collective. I think you saw this Josh where they went and they talked to a whole bunch of enterprise organizations. And this was last year. I mean, this was in 2023. They went to large organizations of a thousand employees or more. That’s not small companies, right? 43% of those companies are planning on hiring technology consultants to advise them. They’re already planning on needing to utilize outside help. And these are large scale organizations. That percentage is going to do nothing but increase as you deal with mid sized or mid market or you know, small business enterprise companies. They’re going to be even more needing this assistance. And so go in and just say, all right. What is your AI strategy? Every company that I’m talking to and you know, whether you are not just going and say it like this, every company I’m talking to has been working on their AI strategy. And I’ve been having conversations with them to figure out their AI roadmap. What is it that you have going on? What projects are you working on right now? Where are you looking to have AI make a difference? And that will just open the floodgates because they’ll either know what they’ve got, they’ll lay it out for you. You can identify areas where they might be able to have some help or they’ll ask for your help in certain areas. Or they might just look at you like a deer stuck in headlights and go, we don’t know and we’re scared to figure it out. Can you help us? And there you go. You know, dive right into it and start helping them out. Love it. Yeah, I mean, it’s a resounding yes. It goes back to when we started doing security years ago. The same the same track was around frameworks, right? We have a belief that frameworks are out there for a reason, whether it’s NIST or whatever it might be. There’s bodies of organizations that that have standards that say these are the things that you should care about in security. This is a framework. This is a roadmap. This is a successful security rollout. And it was the same conversation that we started with security. We would ask if we get on a discovery call. We’d asked a customer, hey, you know, you know, with regard to security, is there a framework that you’re following? You working on NIST and you know, whatever CMMC. And the track was either a what’s a framework, which is a perfectly acceptable answer or be yeah, I’m working on NIST and I need some help with backup and DR and immutable data or I need some help with, you know, this on my endpoints or whatever. I just haven’t been able to get to that as yet. It’s no different, right? I mean, the same we used to always talk about this semi imaginary person, Timmy and I.T. Timmy and I.T. is tasked with a lot of stuff and forever it was I.T. and then 30% of his time it was security and that’s who’s protecting America’s organizations.

And so at the end of this, everybody needs help. And I just think, too, the timing on this is so beautiful because how many times have we talked about the job shortage in security, right? The millions of unfilled jobs. So it’s not like those all got filled. And now we’re out looking for AI people and prompt engineers and things like that. No, security jobs haven’t even gotten filled yet either. Guess what? It just got even harder for the customers. So now they need your help more than ever. And I think what you’ll find is that it’s not like, you know, you’re not going to get a job.

And so what you’ll find in these discovery calls is to your point and to kind of that track we were just talking about, it doesn’t really matter where they are. Great. Are you open if I set up a call to see if there’s a way that we can help you, right? If there’s if there’s initiative and there’s interest and they’ve been tasked with this, they got to come back with some results. So love that we can help them in any of those frames.

Alright, final couple thoughts here, as we get towards the tail end of this. So keep in mind that I want people to look at AI is not this scary Hollywood, it’s going to kill all our jobs. Not true. Keep in mind that AI is framed up, I believe, to look at the tasks that certain things and certain people and certain roles are doing. And can AI augment and make somebody’s job more efficient by automating some of these repetitive tasks. And so my challenge to the partners out there is understand the tasks, you know, ask use the track that you just mentioned, but also understand the prospects that you’re targeting the existing customers, and what the roles are for the points of contact that you have and who they manage. What are the tasks that some of those people are doing look up and do some googling on what those roles are? And what are some of those tasks that those folks are required to do on a daily basis. And those are little tiny slivers here and there that I think people that aren’t sure where AI might fit is where we can kind of lead people to how I might help them right and where that productization might make sense for them. But you know, what’s, what’s your perspective on if I’m a business out there? And I fail to adopt this, I fail to go, all right, I guess I’ll pay attention to this, right? Maybe I don’t even say that. But what’s, what’s your, what’s your read on if the organization’s out there, if the businesses just stick their head in the sand on this?

Well, I think, quite frankly, I think competition will take care of that. I think you’ll see a survival of the fittest AI is a, it’s a considerable phenomenon in that it is making people more efficient. It’s making processes a lot easier. It’s able to automate those different things that are repeatable that, you know, that won’t necessarily need someone that could be error prone to do. The bottom line is, is that if you don’t start working with AI now, you’re going to have an even harder time trying to implement AI later on. This, this is the time period where you need to get to know what’s going on with it now. Maybe you don’t identify use cases that you can actually use. Maybe it’s not quite cost effective, but at least you’re looking into it, at least you’re staying up on it so that when that next possibility does happen, you’re comfortable exploring it. You’re familiar enough with it that you can go forward. And, you know, so the risk that these organizations face, they’re going to become obsolete. You know, like we mentioned before, with the percentage of companies that are looking for AI specifically to keep up with trends or to keep up with their competitors. What that means is there is a large number of competitors out there that are already utilizing AI to make their companies bigger. You know, it’s really interesting. Josh, just a couple of days ago,

JP Morgan Chase, one of the top competitors, you know, they’re going to be able to do a lot of things. JP Morgan Chase, one of the big wigs of JP Morgan Chase, released a letter talking about the impact of AI and kind of basically said AI is not a fad. AI is here to stay. This is an inflection point in human history. And here at Chase, we’re doing it. They’ve already identified within JP Morgan Chase 500 different use cases where machine learning and deep learning can actually have an assistive role. And they’ve hired 2000 people to be AI experts and to make things happen within JP Morgan Chase. So if the big guys are doing it because they recognize how wonderful it’s going to be and how much it’s going to give them a competitive edge, even the smaller organizations, they just, whether they can implement it or not, they’ve got to keep up on it. And if they don’t, they’re going to become obsolete really quick. Fair. Fair. We’ll definitely help them level up. All right. Final question here. JLo, you’re bust out your Miss Clio crystal ball here to the best of your abilities. I always love a good Miss Clio reference. Everybody knows that. Where do you see AI heading in the future? It’s the most nebulous question I’ve ever asked on this podcast, but your thoughts, man.

Well, I think AGI is coming soon. And I think that it’s coming at a really interesting time because there are two to other different types of technological advancements that coupled with AI becoming or approaching the AGI level of capability is going to really transform our world. One of those is quantum computing. Quantum computing is going through some advancements that’s going to make it so that these pieces of latency and processing and things like that that I are going through right now virtually disappear because quantum computing is going to allow AI entities to do things at the speed of the human brain, if not so much faster, so much faster.

That’s the first thing. The second thing coupled with AI that’s going to be pretty fantastic are the advancements in robotics, specifically bipedal robotics or those types of robots that are meant to do things that human beings can do. And I really think that, you know, if we right now, right now we can take a robot, stick it in a room, have it watch people folding clothes for a couple of hours and then we can say, OK, go fold clothes. And it goes and it folds close.

It’s able to do that right now. Today, that’s something that is happening. And so robotics is going to change everybody’s life in a real significant way within the next few years, I think. And it’s going to be driven by AI. And if we reach the level of AG, the level of AGI and we instill that entity in a robotics body, what’s that going to look like? How’s that going to change our world? How’s that going to make human beings’ lives enhanced? It’s a really fun and exciting time. But that’s where I see things happening. I love it. I love it. Yeah, I will tell you, I think over the years we’ve seen that. What’s the robotic dog? Is it Boston Dynamics? Yes. Is that how it makes the robotic dog? It was mind blowing to me. And geez, this was a little while ago. But, you know, walking through a big manufacturing facility with an end customer. And they said, “Oh, yeah, we just we bought the Boston Robotics dog to kind of sniff out air leaks across the equipment.” And everybody goes home and he just kind of roams the facility and points out geographically where there’s air leaks in our equipment. And we just tackle that, right? So we know about the air leaks or the issues before anybody else does or before the equipment fails. And so it’s so cool to see the customers adopting this to get into these conversations. And so awesome stuff. We’ll definitely have to have you back on. Do more of these.

Appreciate you coming on, J.Lo. Thanks so much for coming on in. Thank you, Josh. You know, I always love talking to you and stuff with you specifically. It’s a lot of fun.

Love it. All right. Well, everybody, if you haven’t gone to your favorite choice, Spotify, Apple Music, wherever you’re going, go find the Next Level BizTech podcast. Like and subscribe. We hope to see you out there. We hope to keep creating more content and let us know what you want to see. More things like this, different things, whatever it might be. But I’m your host, JoshLupresto SVP of Sales Engineering atTelarus Mr. Jason Lowe, Solution Architect. We’re talking Mastering AI Strategies for Tech Advisors.

Next Level BizTech has been a production ofTelarus Studio 19. Please visit Teleris.com for more information.